Technical Lead, Data Science

About this role

<p><strong>What is Cobre, and what do we do?</strong></p> <p>Cobre is Latin America’s leading instant b2b payments platform. We solve the region’s most complex money movement challenges by building advanced financial infrastructure that enables companies to move money faster, safer, and more efficiently.</p> <p>We enable instant business payments—local or international, direct or via API—all from a single platform.</p> <p>Built for fintechs, PSPs, banks, and finance teams that demand speed, control, and efficiency. From real-time payments to automated treasury, we turn complex financial processes into simple experiences.</p> <p>Cobre is the first platform in Colombia to enable companies to pay both banked and unbanked beneficiaries within the same payment cycle and through a single interface.</p> <p>We are building the enterprise payments infrastructure of Latin America!</p> <p>&nbsp;</p> <p><strong>What we are looking for:</strong></p> <p>As Technical Lead, Data Science, your mission isn't to ship the fastest model to production for its own sake — it's to make sure every model, every metric, and every analytical decision coming out of the team is built on solid mathematical and statistical foundations. You'll be the technical benchmark that raises the team's rigor: challenging assumptions, questioning methodologies, and ensuring that inference — not just prediction — is done correctly.</p> <p>This is a hands-on lead role, not a purely advisory one. You're expected to review and mentor, yes — but just as often to open the notebook yourself, rework a flawed model specification, or step into a squad's project directly when a methodological blocker needs to be resolved rather than explained. Guidance is the default mode; rolling up your sleeves is what happens when a deadline, a broken assumption, or a stuck teammate requires it.</p> <p>Unlike a role centered on MLOps or deployment infrastructure, your territory is statistical design, model validity, and the quality of quantitative thinking. You'll provide technical leadership — without necessarily having direct managerial reporting from the whole team — to data scientists who are embedded in specific projects and squads (Risk, Liquidity, Payments, among others), acting as their technical reference point, hands-on collaborator, and methodological quality auditor, while they remain aligned day-to-day with their squad's priorities.</p> <p><strong>What would you be doing:</strong></p> <ul> <li>Methodological rigor: Define and enforce standards of statistical and mathematical rigor for all of the team's work — experimental design, hypothesis testing, model selection, validation, and treatment of bias and uncertainty.</li> <li>Distributed technical leadership: Act as the technical (not hierarchical) leader of data scientists embedded in different teams/projects — reviewing their work, and directly stepping in to fix, rebuild, or co-build a model or analysis yourself when a methodological blocker needs hands-on resolution rather than a review comment.</li> <li>Model review and validation: Conduct critical peer reviews of statistical and machine learning models before implementation, checking assumptions, robustness, statistical power, and the validity of conclusions.</li> <li>Advanced analytical design: Design and guide the application of advanced statistical methods (time series, causal inference, optimization, Bayesian models, applied probability theory) to business problems such as risk, liquidity, fraud, and pricing.</li> <li>AI-augmented delivery: Champion and personally use AI-assisted tools and workflows (e.g., AI coding assistants, LLM-driven scaffolding and analysis agents) to speed up the data science lifecycle — from exploration to model validation — helping the team shorten release timelines without cutting corners on rigor.</li> <li>Mentorship and talent development: Mentor junior and senior data scientists on mathematical/statistical fundamentals, raising the team's collective technical bar and fostering a culture of rigorous thinking.</li> <li>Insight communication: Communicate complex analytical results — including their limitations and uncertainty — clearly to both technical and non-technical stakeholders, including senior leadership.</li> <li>Standards and frameworks: Contribute to defining internal analytical quality frameworks (documenting assumptions, statistical acceptance criteria, review checklists) that the CoE can apply across teams.</li> <li>Cross-functional collaboration: Work closely with Product, Risk, Engineering, and Compliance to ensure delivered models solve the right business problem with an appropriate level of statistical confidence.</li> </ul> <p><strong>What do you need:</strong></p> <ul> <li>Education: Master's or Ph.D. in Statistics, Mathematics, Physics, Econometrics, Machine Learning, or a related quantitative field.</li> <li>Quantitative rigor: Deep command of inferential statistics, probability, experimental design, and causal inference — not just applying libraries, but understanding why a method is (or isn't) valid for a given problem.</li> </ul> <p><em>Technical experience:</em></p> <ul> <li>Strong coding skills in Python.</li> <li>Experience with data infrastructure tools like Snowflake.</li> <li>Proficiency with standard ML libraries.</li> <li>Experience with deep learning frameworks like TensorFlow and PyTorch, specifically for training and fine-tuning large language models (LLMs).</li> <li>Conceptual knowledge of MLOps (model lifecycle, monitoring, versioning) — hands-on deployment/infrastructure experience is not required, but you should understand the topic well enough to speak the same language as those who execute it.</li> <li>Domain expertise: Experience applying quantitative models to risk, fraud, liquidity, or financial analytics problems, ideally in fintech or financial services.</li> <li>Technical leadership: Demonstrated experience providing technical leadership to other data scientist